This is the first post of a three-part series regarding Knowledge Management. The second post is Knowledge Management: The Process and Getting Buy-In, and the third is Choosing a KMS Tool: Which Way to Go?
Knowledge Management. You’ve heard this term in meetings, in TED Talks, or when networking, but perhaps you really don’t understand what it means. Well, it’s time to learn because knowledge management (KM) significantly affects how companies operate, especially now that so many have transitioned to a hybrid or fully remote environment, The McKinsey Global Institute reports that a top-notch knowledge management system (KMS) can raise organization-wide productivity by 20 to 25 percent. The same report indicates that a KMS can reduce the time employees search for information by up to 35 percent. What’s not to like?
The Role of Artificial Intelligence in Knowledge Management
The integration of AI in KM has transformed how organizations handle their knowledge resources. AI enhances KM by automating routine tasks, improving search capabilities, and providing predictive insights.
AI-Powered Search and Retrieval
AI technologies, such as natural language processing (NLP) and machine learning (ML), have revolutionized search functions in KMS. Using NLP to interpret human language and context enables faster and more accurate retrieval of information. In a study evaluating the impact of NLP when searching medical documents, 93 percent of respondents agreed that NLP-enhanced search would make clinical workflows more efficient than string search.
Enhancing Explicit, Implicit, and Tacit Knowledge with AI
Simply stated, knowledge management refers to how your organization creates, accesses, shares, and maintains your resources. Instead of wasting time trying to figure out who to ask for information, then tracking that person down, you need only check the KMS to find the answer to those questions asked over and over.
- Explicit knowledge. AI can automatically categorize and tag documents, making it easier to store and retrieve formal documentation like FAQs, instructions, and reports. Advanced algorithms can also identify and highlight key information within documents, streamlining the knowledge-sharing process.
- Implicit knowledge. AI systems can analyze patterns and trends in data to offer recommendations and best practices. For instance, AI can monitor how experienced employees solve problems and suggest these methods to others, effectively sharing implicit knowledge.
- Tacit knowledge. AI chatbots and virtual assistants facilitate real-time communication and quick access to information, capturing and disseminating tacit knowledge. These tools can answer common questions, provide context-based responses, and learn from interactions to improve over time.
AI in Remote and Hybrid Work Environments
In remote and hybrid work settings, AI enhances KM by ensuring seamless access to information regardless of location. AI-driven platforms enable employees to collaborate efficiently, maintain productivity, and stay connected. For example, AI can recommend relevant documents during virtual meetings or suggest experts within the organization who can provide insights on specific topics.
AI and Knowledge Consistency
Maintaining up-to-date and accurate information in your KMS is crucial. AI can automate the updating process by identifying outdated information and suggesting updates or corrections. This ensures that your KMS remains a reliable resource, even as employees and systems change.
Security and Privacy Considerations
AI systems in KM must be designed with security and privacy in mind. Advanced AI can help detect and prevent data breaches, ensuring that sensitive information remains protected. Additionally, AI can enforce access controls and monitor usage patterns to prevent unauthorized access to knowledge resources.
Example of an AI-Enhanced KMS
One famous KMS is IBM’s Watson, featured several years ago on “Jeopardy.” “Watson Discovery is AI-powered search that uses natural language processing (NLP) to retrieve answers and uncover insights buried in documents, webpages, and big data. Watson Discovery cuts down search time by more than 75 percent.” (IBM) Watson interprets human language and understands context, using this understanding to quickly find relevant documents and webpages.
Consistency is Key
These are the types of knowledge you need to collect, store, and share with your organization. But that’s not the end: consistently updating your knowledge base is extremely important. If your new hire uses your current billing system but the only information in your KMS is for the previous system, that information is useless. The new biller now needs to figure out the system by trial and error or take time from a co-worker who, hopefully, knows something about it. When employees leave, they take a treasure trove of knowledge with them. It’s imperative to keep that knowledge in the KMS so that it is not lost forever.
The Future of KM with AI
The integration of AI in KM is just the beginning. Future advancements will likely bring even more sophisticated tools that can anticipate organizational needs, provide deeper insights, and facilitate more intuitive interactions with knowledge systems.
The most important aspect of knowledge management is using it. It’s great to have a KMS in place, but it does no good if an organization does not train employees how to use it and consistently reinforces that training. Setting up and using your KMS now means you’ll be ready for the next bump in the road. If your staff works from locations around the country – or globe – easy access to this information is imperative. Don’t let all that knowledge go to waste!
Do you need a knowledge management system or want to improve the one you currently use? We can help you determine your best options according to your needs and budget! Contact us today to learn more!
Related Blogs
Knowledge Management: The Process and Getting Buy-In
Choosing a KMS Tool: Which Way to Go?
AI-Powered Knowledge Management: Productivity and Innovation
Resources
“20+ best AI quotes to inspire IT professionals.” Atera. 3/28/24. Accessed 7/17/24. https://www.atera.com/blog/best-ai-quotes/
Eliyahu, Sagi. “Knowledge management in the hybrid work era: 4 key insights.” KMWorld. 3/18/22. Accessed 7/17/24. https://www.kmworld.com/Articles/Editorial/ViewPoints/Knowledge-management-in-the-hybrid-work-era-4-key-insights-151972.aspx
Park, Eunsoo H., Hannah I. Watson, Felicity V. Mehendale, and Alison Q. O’Neil. “Evaluating the impact on clinical task efficiency of a natural language processing algorithm for searching medical documents: Prospective crossover study.” Medrxiv. 6/1/22. Accessed 7/1/24. https://www.medrxiv.org/content/10.1101/2022.05.24.22275490v1.full
“The social economy: Unlocking value and productivity through social technologies.” McKinsey Global Institute. 7/1/12. Accessed 7/17/24. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-social-economy
“What is knowledge management?” IBM. Accessed 7/17/24. https://www.ibm.com/cloud/learn/knowledge-management
